Object tracking, or video tracking, is the process of locating and monitoring an object that is moving using images captured by a camera. This form of tracking can be used in a variety of fields, including entertainment, security, communications, augmented reality, law enforcement, military defense, medicine and other scientific endeavors. The object tracking process can be time consuming given the large amounts of data inherent in video capture. Object tracking aims to relate objects between consecutive frames of a video. Various algorithms may be used to analyze video frames for the object's movement between frames. Two basic parts of object tracking include detecting the object and associating detections of the same object over time. Challenges can arise in the case of objects that are fast-moving or change orientation. To handle these situations, motion models may be employed to describe potential changes in the object's appearance or trajectory.
Various object recognition techniques may be employed in the context of object tracking to locate and identify objects in an image sequence, using computer vision. Object recognition seeks to mimic the human ability to easily recognize objects despite variables such as different viewpoints, sizes, rotations, and partial obstruction. Many approaches have been presented, including appearance-based methods and feature-based methods. Appearance-based methods use exemplars, or template images, of an object to represent a multitude of appearances of the object and perform recognition accordingly. The exemplars may represent the object under varying conditions such as lighting, viewing direction, size and shape. Feature-based methods aim to match target object features with image features, such as surface patches, corners and/or linear edges.